A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data
Study region: Hetao Irrigation District, China Study focus: Reference crop evapotranspiration (ETo) estimation is crucial for further calculating crop evapotranspiration and supporting water allocation decisions. The Penman Monteith 56 (P-M 56) formula is considered as the standard method for estima...
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2025-10-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825005385 |
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| author | Xiao Zhang Yuxin Tao Chenglong Zhang |
| author_facet | Xiao Zhang Yuxin Tao Chenglong Zhang |
| author_sort | Xiao Zhang |
| collection | DOAJ |
| description | Study region: Hetao Irrigation District, China Study focus: Reference crop evapotranspiration (ETo) estimation is crucial for further calculating crop evapotranspiration and supporting water allocation decisions. The Penman Monteith 56 (P-M 56) formula is considered as the standard method for estimating ETo but requires extensive meteorological data, limiting its use in data-scarce regions. In response to this concern, this study proposed two integrated deep learning models, i.e., CNN-Transformer and CNN-Informer, to estimate ETo based on three meteorological factor input combinations (temperature-based, radiation-based, and mass transfer-based). New hydrological insights for the region: The results indicated that the mass transfer-based CNN-Informer3 model achieved the best estimation performance, with its average Determination Coefficient (R2), Nash-Sutcliffe efficiency coefficient (NSE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Mean Absolute Error (MAE) and Percentage Bias (PBIAS) of 0.9712, 0.9665, 0.3764 mm/d, 0.1192, 0.2591 mm/d and -3.8143 % for accuracy evaluation, significantly outperforming other models. In conclusion, the key contribution of this study is the proposal of a hybrid deep learning framework that expands the applications of emerging deep learning algorithms and ensemble learning in the field of evapotranspiration estimation, which enables a more precise matching between irrigation water extraction and actual crop needs. Regional hydrological processes can thus better adapt to natural fluctuations, reducing the risks of droughts (due to insufficient irrigation) or waterlogging (due to excessive irrigation). |
| format | Article |
| id | doaj-art-2af13398049b4f5aa83b0692f9ed06b3 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-2af13398049b4f5aa83b0692f9ed06b32025-08-20T05:06:56ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-10-016110270910.1016/j.ejrh.2025.102709A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological dataXiao Zhang0Yuxin Tao1Chenglong Zhang2State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China; School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China; Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, ChinaState Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China; Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China; Corresponding author at: State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China.Study region: Hetao Irrigation District, China Study focus: Reference crop evapotranspiration (ETo) estimation is crucial for further calculating crop evapotranspiration and supporting water allocation decisions. The Penman Monteith 56 (P-M 56) formula is considered as the standard method for estimating ETo but requires extensive meteorological data, limiting its use in data-scarce regions. In response to this concern, this study proposed two integrated deep learning models, i.e., CNN-Transformer and CNN-Informer, to estimate ETo based on three meteorological factor input combinations (temperature-based, radiation-based, and mass transfer-based). New hydrological insights for the region: The results indicated that the mass transfer-based CNN-Informer3 model achieved the best estimation performance, with its average Determination Coefficient (R2), Nash-Sutcliffe efficiency coefficient (NSE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Mean Absolute Error (MAE) and Percentage Bias (PBIAS) of 0.9712, 0.9665, 0.3764 mm/d, 0.1192, 0.2591 mm/d and -3.8143 % for accuracy evaluation, significantly outperforming other models. In conclusion, the key contribution of this study is the proposal of a hybrid deep learning framework that expands the applications of emerging deep learning algorithms and ensemble learning in the field of evapotranspiration estimation, which enables a more precise matching between irrigation water extraction and actual crop needs. Regional hydrological processes can thus better adapt to natural fluctuations, reducing the risks of droughts (due to insufficient irrigation) or waterlogging (due to excessive irrigation).http://www.sciencedirect.com/science/article/pii/S2214581825005385Regional hydrologyReference crop evapotranspirationIntegrated deep learning modelMeteorological factor input combinationsHetao Irrigation District |
| spellingShingle | Xiao Zhang Yuxin Tao Chenglong Zhang A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data Journal of Hydrology: Regional Studies Regional hydrology Reference crop evapotranspiration Integrated deep learning model Meteorological factor input combinations Hetao Irrigation District |
| title | A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data |
| title_full | A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data |
| title_fullStr | A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data |
| title_full_unstemmed | A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data |
| title_short | A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data |
| title_sort | hybrid deep learning framework for regional reference crop evapotranspiration estimation in the hetao irrigation district using limited meteorological data |
| topic | Regional hydrology Reference crop evapotranspiration Integrated deep learning model Meteorological factor input combinations Hetao Irrigation District |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825005385 |
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